AoI驱动的多运营商无人机网络协作资源共享分层学习
AoI-Driven Hierarchical Learning for Cooperative Resource Sharing in Multi-Operator UAV Networks
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- University of Waterloo（滑铁卢大学）
- The University of Sydney（悉尼大学）
- Kuwait College of Science and Technology (KCST)（科威特科学与技术学院）
- Carleton University（卡尔顿大学）
- Tohoku University（东北大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对多运营商无人机网络中协作资源共享与轨迹控制的NP难问题，提出AoI驱动的分层D3QN框架，通过中断补偿和负载平衡机制，在严重拥塞下平均AoI降低56.1%，并提升公平性。
中文摘要 AI 辅助
无人机（UAV）辅助网络为按需连接提供了一种通用范式。然而，在多运营商空中网络（MOANs）中，协作资源共享与三维轨迹控制的联合优化以维持信息新鲜度是一个复杂的组合问题，可证明为NP难问题。为解决这一计算复杂性，我们提出了一种信息年龄（AoI）驱动的分层深度强化学习（DRL）框架。具体而言，在运营商和无人机决策层均部署了Dueling Double Deep Q-Network（D3QN）架构，以减轻过估计偏差并增强高维状态空间中的稳定性。为提高系统韧性，我们引入了一种基于AoI和负载感知的中断补偿机制，该机制根据瞬时传输需求和时间新鲜度对用户进行优先级排序。此外，我们纳入了一个归一化负载交换平衡指标，以调节协作行为并确保跨运营商的资源公平性。仿真结果表明，所提出的分层D3QN显著优于传统DRL、非协作和协作基准，在严重拥塞情况下平均AoI降低高达56.1%，同时确保了优越的运营商间公平性和中断缓解能力。
英文摘要
Uncrewed aerial vehicle (UAV)-assisted networks provide a versatile paradigm for on-demand connectivity. However, in multi-operator aerial networks (MOANs), the joint optimization of cooperative resource sharing and 3D trajectory control to maintain information freshness is a complex combinatorial problem, which can be shown to be NP-hard. To address this computational complexity, we propose an age of information (AoI)-driven hierarchical deep reinforcement learning (DRL) framework. Specifically, a Dueling Double Deep Q-Network (D3QN) architecture is deployed at both the operator and UAV decision layers to mitigate overestimation bias and enhance stability in high-dimensional state spaces. To improve system resilience, we introduce an AoI- and load-aware outage compensation mechanism that prioritizes users based on instantaneous transmission demands and temporal freshness. Furthermore, a normalized load exchange balance metric is incorporated to regulate cooperative behavior and ensure resource fairness across operators. Simulation results demonstrate that the proposed hierarchical D3QN significantly outperforms conventional DRL, non-cooperative, and cooperative benchmarks, reducing the average AoI by up to 56.1% under severe congestion while ensuring superior inter-operator fairness and outage mitigation.
